Introduction to Data Cleansing, Its Benefits, & Quality Issues

data cleansing

Did you know 30 percent of a company data becomes obsolete every year.  each year, according to a source?

The world is frequently changing, especially in the corporate world, where things are rapidly becoming digital. Digitalisation cannot be established unless you have data. This data should be neat and clean. Certainly, records, files, documents, or any piece of information can be there, but in a raw format, and sometimes, they can be obsolete. It means that information is there with lots of errors, inconsistencies, missing details, and duplicate entries. Now that you have it, all you need to do is scrub it. This process is called data cleansing.

Data Cleansing

Typically, data cleansing is an ongoing process that starts with detecting flaws, fixing them, and preventing errors, duplicates, and imperfections in a database. Since it’s a process, the data cleansing experts follow a well-defined strategy that targets achieving accuracy, reliability, and consistency. With these qualities, the data becomes useful. Its clean structure helps in getting deep into insights, preparing exact reports, and making decisions that actually prove helpful and actionable.

Let’s take the case of an online marketing department that mainly focuses on collecting customer data from multiple sources. These sources can be online forms, emails, sales transactions, cart status, web journeys, etc.

The marketing specialists collate these details and immerse themselves in them to discover flawed entries like wrong names, different date formats of birth, etc. This cleaning process guides how to identify, merge, or eliminate duplicate data, fix typos, and standardise the entire format. This is how the entire data cleansing process takes care of your data, which is going to be the basis of your decisions.

Benefits of Data Cleansing

A clean database is obviously a strength, which benefits organisations and various industries in different ways. Let’s come across these advantages.

1. Improved Data Quality

The very first benefit is infallibility. It shows the premium quality of the information that you store. Error-free data represents high-quality information, which is reliable and helpful. You can discover its benefits by executing the decisions or strategies derived from them. Simply measure the result of that decision. The accurate insights lead to better outcomes like multiplied sales, leads, revenues, etc., which stakeholders appreciate and trust.

2. Enhanced Decision-Making

Error-free databases lay the groundwork for making informed decisions. Let’s say you have duplicate contacts in your merged mailing list. Certainly, this process will help you to remove and fix email addresses that have missing details or wrong syntax. This is also part of this process. By providing accurate contact lists of your customers, you can leverage their power to identify serious buyers, challenges, and opportunities to convert intended users into consumers. This can be possible by deciding who to reach out to (who will obviously be intended users).

3. Increased Operational Efficiency:

The next benefit is associated with operational efficiency, which can be positively or negatively impacted because of erroneous data. You have to align and sometimes, realign your manual resources or cleansing tools to eliminate manual errors. Overall, this cleansing process streamlines data processing and the flow of optimised data entries, which certainly minimize the possibility of reworking. This is how one can automate repetitive processes like copy-pasting, formatting, etc. so that the aligned team can efficiently operate and prove more productive.   

4. Regulatory Compliance

Privacy policies and compliance keep the majority of the corporate world regulated. It means that companies have to follow the regulatory requirements related to data accuracy and integrity as mentioned in the robust rulebook for compliance called GDPR< HPAA, and SOX. These regulations make it mandatory to keep or provide accurate records without compromising sensitive information.

For example, a healthcare provider like a clinic owner must maintain data and its accuracy, as mentioned in HIPAA. This regulation is set up to protect patients’ sensitive data from hackers or unauthorised users. Also, it ensures that the data won’t be tampered with. With this process, the data owner continues to detect and fix errors in those records, which can lead to incorrect diagnoses, and medicines. This is how it can maintain its compliance with HIPAA standards.

5. Saving Money

This procedure helps you save your hard-earned money. Certainly, bad data misleads. The scrubbed entries are free from errors, dupes, or inconsistencies, which guide organisations to avoid mistakes like sending inaccurate products to specific addresses, or making inaccurate financial forecasts, etc. These kinds of mistakes would cause the loss of money and resources, which would definitely impact the efficiency of your staff members. Also, you see your brand reputation at stake. People no longer feel happy, which eventually results in minimal revenues and low profit margins.  

Common Data Quality Issues Resolved Through Data Cleansing

By filtering out errors, one can address quality issues. The root causes of these errors can be human errors, system incapacities, and challenges in integrating data. Let’s figure out some common data quality issues here.

1. Errors

Errors are mistakes that can happen when recording data incorrectly. It leads to multiple discrepancies in the dataset. Specifically, these errors can be typos, incorrect values, or missing values.

Let’s say a sales transaction is inaccurately recorded, which is a typo. This process helps in filtering those wrong entries to fix them so that the transactions can be accurate.

2. Inconsistencies

The next concern is inconsistencies, which arise when your data has variations in its structure. It looks different from other sources or systems, which makes it challenging to reconcile and accurately analyse. This can be the case after web scraping, which has lots of HTML data. Additionally, some naming conventions, data formats, and measuring units can be different in format, causing inconsistencies.

Let’s consider the case of an inventory, that has refined oil’s quantity is recorded in liters and milliliters. This variation can make it challenging to discover the exact quantity or stock value.  This is where the data cleansing solutions prove to be a milestone.

3. Duplicates

Duplicates can be called dupes, which is the condition of recording an entry multiple times in a dataset. These entries are also called redundant values. This mistake happens when you migrate a database from one place to another or import-export data.

4. Incomplete or Missing Values

Missing entries represent incomplete data, which is also a common issue. It represents the gap that adversely impacts the understandability and reliability of data or insights. Data cleansing fills this gap by integrating or enriching the values complementing existing (incomplete) details in a database. Its result can be seen in improved decisions. The organisation becomes able to extract valuable insights, identify trends, and also make informed decisions that drive the success of any business.


Data cleaning or cleansing can be called a key process that helps in making decisions that actually work and prove a milestone. This process helps transform businesses through decisions that realistically work. Likewise, there are multiple benefits and challenges that are thoroughly mentioned above.

Related Posts